TY - GEN
T1 - Prospective prediction and control of image properties in model-based material decomposition for spectral CT
AU - Wang, Wenying
AU - Tivnan, Matthew
AU - Gang, Grace J.
AU - Stayman, J. Webster
N1 - Funding Information:
This work was supported, in part, by NIH grant R01EB025470 and R21EB026849.
Publisher Copyright:
© 2020 SPIE.
PY - 2020
Y1 - 2020
N2 - Model-based material decomposition (MBMD) directly estimates the material densities from the spectral CT data and has found opportunities for dose reduction via physical and statistical modeling and advanced regularization. However, image properties of material basis volumes can be complex. For example, spatial resolution, noise, and cross-talk can depend on acquisition parameters, regularization, patient size, and anatomical target. In this work, we propose a set of prospective prediction tools for the generalized local impulse response (LIR) that characterizes both in-basis spatial resolution and cross-basis response, as well as noise correlation. The accuracy of noise predictor was validated in a simulation study, comparing predicted and measured in- and cross-basis noise correlations. Employing these predictors, we composed a specialized regularization for cross-talk reduction and showed that such prediction tools are promising for task-based optimization in spectral CT applications.
AB - Model-based material decomposition (MBMD) directly estimates the material densities from the spectral CT data and has found opportunities for dose reduction via physical and statistical modeling and advanced regularization. However, image properties of material basis volumes can be complex. For example, spatial resolution, noise, and cross-talk can depend on acquisition parameters, regularization, patient size, and anatomical target. In this work, we propose a set of prospective prediction tools for the generalized local impulse response (LIR) that characterizes both in-basis spatial resolution and cross-basis response, as well as noise correlation. The accuracy of noise predictor was validated in a simulation study, comparing predicted and measured in- and cross-basis noise correlations. Employing these predictors, we composed a specialized regularization for cross-talk reduction and showed that such prediction tools are promising for task-based optimization in spectral CT applications.
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U2 - 10.1117/12.2549777
DO - 10.1117/12.2549777
M3 - Conference contribution
C2 - 33162639
AN - SCOPUS:85086712311
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2020
A2 - Chen, Guang-Hong
A2 - Bosmans, Hilde
PB - SPIE
T2 - Medical Imaging 2020: Physics of Medical Imaging
Y2 - 16 February 2020 through 19 February 2020
ER -